Problem drinkers (PDs) represent a majority of the estimated 32 million Americans with alcohol problems that spans a spectrum of severity from individuals who drink excessively and experience of occasional negative consequences to those with moderate AD and intact psychosocial functioning. PDs can benefit from relatively brief treatment that could be delivered in mainstream healthcare, but less than 5% receive such care. In addition, PD treatment is only modestly effective, and there is a surprising absence of empirical research to guide PD treatment selection. Adaptive Interventions (AI) are a novel approach to treatment development that may have significant advantages over fixed treatments in improving efficacy and fostering adoption of EBPs in mainstream healthcare. Over the last decade, important advances have been made in AI development methods including Sequential Multiple Assignment Randomized Trials (SMART) and control engineering (CE) designs. These advances have yielded important discoveries in the treatment of other chronic illnesses but are just beginning to be applied to AUD. This study proposes to combine SMART, CE, and ecological momentary assessment to develop an AI for PD that can be used in mainstream healthcare. If study aims are achieved, a set of empirically-derived decision support tools will be created to guide AUD care similar to tools that exist for other chronic diseases. In addition, new knowledge will be gained about MOBC of AUD that can guide future AUD treatment research. Finally, important progress will be made in methods that capitalize on the remarkable advances in sensor technologies, advanced mathematics, and engineering to create a new type of tailored, near-real time feedback, adaptive behavior therapies